When Gottfried Wilhelm Leibniz arrived in the English capital for the first time in 1673, a crowd of people awaited him on London Bridge. But the curious Londoners were less interested in the persona of the German scholar, but rather in any information about the Dutch-English War, which he might have picked up in the course of his passage. While Leibnitz reports on what he has heard, a man breaks from the crowd and is immediately surrounded by a couple of barefooted boys jumping up and down.

He scribbles something on a bit of paper and handed it to the one who jumps up highest.
This one spun, forced a path through the others, took the stairs four at a time, broke loose onto the Square, vaulted over a wagon, spun a fishwife, and then began to build speed up the bridge. From here to the London shore was a hundred and some yards, from there to the Change was six hundred – he’d be there in three minutes. [1]

This fictional scene taken from Neil Stephenson's book Quicksilver may appear like some kind of game to an uninformed observer. But what we witness here is driven by economic intention. The valuable information is exchanged for money at the London stock exchange. Leibniz had reported gunshots, but he implied – long after the boy had ran off – they were not those of the action of war but rather “coded data, speeding through the fog so opaque to light but so transparent to sound–”

On the 2nd of March 1791, about eighty years after Leibniz's death, the Frenchman Claude Chappe invented the first functioning system that optically transmitted complex messages over distance. “Si Vous reussissez, vous serez bientot couvert de gloire." (“If you succeed, you will be covered in glory.”) – so ran the first message to be sent. The apparatus was named 'télégraphe'. In 1830 there was a network of roughly 1000 optical telegraph towers across Europe, allowing messages to be carried from Paris to Amsterdam, or from Brest to Venice. At its highpoint in 1850 the French system alone involved 29 cities, 556 stations, and covered roughly 4800km. Good visibility was critical to the functionality of the system. If it was foggy, there was nothing to report, as we can learn from Kazys Varnelis. [2]

Today it is common practice to transact business under obscured conditions. Trading algorithms constantly race through global networks of underground cable systems. Stocks of varying trade value in different commercial venues, are bought cheaply in one and sold at highest rate in another place, until differences are levelled out. Sometimes, the interdependences between stocks are direct: If the price for crude oil increases, it is very probable that the price for oil companies will do so as well. If an algorithm wants to earn money, it has to be quicker than its fellow algorithms. So far, so easy. Things get more complicated, if the algorithm does not only run as fast as possible, but at the same watches out for rivals, attempts to hide itself from them, while trying to find out what they are up to.

Modern trading algorithms have long been more than just simple sets of rules for buying and selling stocks. They scan their environment for coherencies – driven by the urge to adapt, they constantly become quicker and more elaborate.

Some processes are only understood after they have ceased to function properly – or have collapsed. The invisible international race of trading algorithms, that we laymen do not really see through, came to light on the 6th of May 2010 in the form of a 'Flash Crash'. The algorithmic sale of 75000 so-called E-mini futures triggered one of the most drastic drops in the history of the American stock market. Within minutes the market recovered. For the first time, high-frequency trading was linked to a stock market crash. Even though the event itself had only lasted a few minutes, numerous private and state actors were kept busy for the next three years, trying to figure out the cause of the phenomenon.

Not all unusual trading activity is immediately visible. When algorithms trade, the market oscillates in millisecond staccatti. Therefore, it is quite common that such unusual activities pass unnoticed at the time of their occurrence. The company Nanex has specialized in scrutinizing historical trading data. Playing children are not always children at play, as Stephenson tells us. In these investigations they discover and collect unusual examples of curious algorithmic trading sequences that they name and present on their website: Castle Wall, To the moon, Alice!, Sunshowers, Broken Highway… .

Considered economically, the graphs remain inexplicable for now. They fail in their purpose to provide information. Like a dadaist poem, the form itself comes to the fore: the rhythm, the shape, the colour. Guided by their titles, the graphs become pictures – frenzied dashes slowly form castle walls. Only, there are no walls. They are just a product of our perception and our desire to understand. Maybe, the graphs give us an inkling of an algorithm’s world. Driven by their will to succeed, they run through the fog; nightly birds become thieves and monsters, transformed by their paranoid gaze. Just like us, they create order and coherence, images and delusions. If today, a human and an algorithm lay in the grass and look up into the sky, both are likely to see faces where there are only clouds. A financial crash, so it seems, is not only a financial but also a semiotic accident.

We look at the graphs and don’t understand them. We find them beautiful. We sense that with every movement of the line, millions of dollars are exchanged through the electronic hands of trading algorithms. What they show will trigger more events. But we cannot know how they will occur, what or whom they will befall, where or when they will take place.

Tempted by their beauty, we try to reinforce their effects. We strip off all information that point to their original context, such as time lines and graph keys. What is left are abstract images that do not represent anything but their colouring, expanse and limitations. On several hundred double page spreads, the image on the right and the title on the left, a collection of modified graphs of past trading events and phenomena is composed. Identification or classification are not important here: The collection only serves viewership purposes.

We observe the graphics again. Now, they speak differently to us. We don't see activies and functions in the abstract images and structures anymore; rather landscapes, tools, journeys into outer space, and sunsets. 75000 futures.

The artbook »75,000 futures« depicts 240 graphs along with their poetic titles that picture high frequency trade sequences of the New York Stock Exchange crash on May 6th, 2010. »The Blue Pig», »Low Tide», »The Circus Comes To Town» and others document how trading algorithms caused an initial collapse of the stock markets . The graphs were collatedand named by the firm Nanex that ultimately identified the cause of the crash: a network problem triggered the sale of 75,000 e-mini futures. The artbook dispenses with explanations. The graphs appear, without time axis and key, as pure forms. What seems insane in economic terms, suddenly makes sense in terms of beauty and poetry.